利用 AthenaK 实现性能便携式双中子星合并

Jacob Fields, Hengrui Zhu, David Radice, James M. Stone, William Cook, Sebastiano Bernuzzi, Boris Daszuta
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引用次数: 0

摘要

我们介绍了 AthenaK 代码的一个扩展,该代码采用 3+1 保守欧拉公式,用于动态时空中的广义相对论磁流体动力学(GRMHD)。与固定时空 GRMHD 求解器一样,我们使用标准有限体积方法来演化流体,并使用约束传输方案来保留磁场的无发散约束。我们还利用一阶通量校正(FOFC)方案来减少对人工大气的需求,并可选择执行最大原则来提高稳健性。我们使用一组平面和曲面时空的标准测试来证明 AthenaK 的准确性。我们使用一个围绕克尔黑洞的 SANE 吸积盘,比较了新求解器和使用所谓 "类 HARM "公式的现有静止时空求解器。我们发现,这两种公式收敛的结果相似。我们还首次公布了在图形处理器(GPU)上进行的双中子星(BNS)合并。得益于FOFC方案,我们的双中子星合并在重子质量守恒方面保持了$\mathcal{O}(10^{-11})$或更好的相对误差,直至坍缩。最后,我们在OLCF前沿上对AthenaK进行了扩展测试,对于动态时空中具有六级网格细化的GRMHD问题,我们显示了高达32768个GPU的$\geq 80\%$效率和高达65536个GPU的$74\%$的出色弱扩展。与CPU相比,AthenaK使用GPU实现了数量级的提速,证明它适合在现代超大规模资源上执行数值相对论问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance-Portable Binary Neutron Star Mergers with AthenaK
We introduce an extension to the AthenaK code for general-relativistic magnetohydrodynamics (GRMHD) in dynamical spacetimes using a 3+1 conservative Eulerian formulation. Like the fixed-spacetime GRMHD solver, we use standard finite-volume methods to evolve the fluid and a constrained transport scheme to preserve the divergence-free constraint for the magnetic field. We also utilize a first-order flux correction (FOFC) scheme to reduce the need for an artificial atmosphere and optionally enforce a maximum principle to improve robustness. We demonstrate the accuracy of AthenaK using a set of standard tests in flat and curved spacetimes. Using a SANE accretion disk around a Kerr black hole, we compare the new solver to the existing solver for stationary spacetimes using the so-called "HARM-like" formulation. We find that both formulations converge to similar results. We also include the first published binary neutron star (BNS) mergers performed on graphical processing units (GPUs). Thanks to the FOFC scheme, our BNS mergers maintain a relative error of $\mathcal{O}(10^{-11})$ or better in baryon mass conservation up to collapse. Finally, we perform scaling tests of AthenaK on OLCF Frontier, where we show excellent weak scaling of $\geq 80\%$ efficiency up to 32768 GPUs and $74\%$ up to 65536 GPUs for a GRMHD problem in dynamical spacetimes with six levels of mesh refinement. AthenaK achieves an order-of-magnitude speedup using GPUs compared to CPUs, demonstrating that it is suitable for performing numerical relativity problems on modern exascale resources.
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